class_docstr: Suggested backoff chain; includes at least on of each
  type of major sequential backoff class from backoff.py
  
  ### Putting it all together
  ### BETA Version of the Backoff Lemmatizer AKA BackoffLatinLemmatizer
  ### For comparison, there is also a TrainLemmatizer that replicates the
  ###    original Latin lemmatizer from cltk.stem
class_docstr_tok: ["Suggested","backoff","chain",";","includes","at","least","on","of","each","type","of","major","sequential","backoff","class","from","backoff.py","#","#","#","Putting","it","all","together","#","#","#","BETA","Version","of","the","Backoff","Lemmatizer","AKA","BackoffLatinLemmatizer","#","#","#","For","comparison",",","there","is","also","a","TrainLemmatizer","that","replicates","the","#","#","#","original","Latin","lemmatizer","from","cltk.stem"]
code: def _randomize_data(cls, train: List[list], seed: int):
      import random
  
      random.seed(seed)
      random.shuffle(train)
      train_size = int(0.9 * len(train))
      pos_train_sents = train[:train_size]
      lem_train_sents = [
          [(item[0], item[1]) for item in sent] for sent in train
      ]
      train_sents = lem_train_sents[:train_size]
      test_sents = lem_train_sents[train_size:]
  
      return pos_train_sents, train_sents, test_sents
  
file_path: cltk/src/cltk/lemmatize/lat.py
imports: import os
  import re
  from typing import List
  from cltk.lemmatize.backoff import DefaultLemmatizer, DictLemmatizer, IdentityLemmatizer, RegexpLemmatizer, UnigramLemmatizer
  from cltk.utils import CLTK_DATA_DIR
  from cltk.utils.file_operations import open_pickle
label_desc: Use image node_id 5 for calling the LatinBackoffLemmatizer obj's underlying member method code with example usage: obj._randomize_data(cls, train, seed) and returns: pos_train_sents,  train_sents,  test_sents